Create discriminatorModel.py

discriminatorModel.py

@@ -0,0 +1,37 @@
+
+import torch
+import torch.nn as nn
+
+# The Discriminator model
+class Discriminator(nn.Module):
+    def __init__(self, channels, embed_dim=1024, embed_out_dim=128):
+        super(Discriminator, self).__init__()
+        self.channels = channels
+        self.embed_dim = embed_dim
+        self.embed_out_dim = embed_out_dim
+
+        # Discriminator architecture
+        self.model = nn.Sequential(
+            *self._create_layer(self.channels, 32, 4, 2, 1, normalize=False),
+            *self._create_layer(32, 64, 4, 2, 1),
+            *self._create_layer(64, 128, 4, 2, 1),
+            *self._create_layer(128, 256, 4, 2, 1),
+            *self._create_layer(256, 512, 4, 2, 1)
+        )
+        self.text_embedding = Embedding(self.embed_dim, self.embed_out_dim)  # Text embedding module
+        self.output = nn.Sequential(
+            nn.Conv2d(512 + self.embed_out_dim, 1, 4, 1, 0, bias=False), nn.Sigmoid()
+        )
+
+    def _create_layer(self, size_in, size_out, kernel_size=4, stride=2, padding=1, normalize=True):
+        layers = [nn.Conv2d(size_in, size_out, kernel_size=kernel_size, stride=stride, padding=padding)]
+        if normalize:
+            layers.append(nn.BatchNorm2d(size_out))
+        layers.append(nn.LeakyReLU(0.2, inplace=True))
+        return layers
+
+    def forward(self, x, text):
+        x_out = self.model(x)  # Extract features from the input using the discriminator architecture
+        out = self.text_embedding(x_out, text)  # Apply text embedding and concatenate with the input features
+        out = self.output(out)  # Final discriminator output
+        return out.squeeze(), x_out